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 "metadata": {
  "name": "",
  "signature": "sha256:ef8eb915e15a9a24f8ab71a194ae76fac764575bddd2d7f4d0c7b0f80cb66457"
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Initializations: Skip to the next section"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets import ImageWidget\n",
      "from IPython.display import display, display_svg, display_png"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# xxxxxxxxxx Add the parent folder to the python path. xxxxxxxxxxxxxxxxxxxx\n",
      "import sys\n",
      "parent_dir = \"../\"\n",
      "sys.path.append(parent_dir)\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxx Import Statements xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "from time import time\n",
      "from time import sleep\n",
      "import numpy as np\n",
      "from pprint import pprint\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "from pyphysim.simulations.runner import SimulationRunner\n",
      "from pyphysim.simulations.parameters import SimulationParameters\n",
      "from pyphysim.simulations.results import SimulationResults, Result\n",
      "from pyphysim.simulations.simulationhelpers import simulate_do_what_i_mean, get_common_parser\n",
      "from pyphysim.comm import modulators, channels, pathloss\n",
      "from pyphysim.util.conversion import dB2Linear, linear2dB\n",
      "from pyphysim.cell.cell import Cluster\n",
      "from pyphysim.util import misc\n",
      "from pyphysim.util.conversion import dBm2Linear\n",
      "from pyphysim.ia import ia\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Set the parameters, create the cluster and the channels, etc"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multiUserChannel = channels.MultiUserChannelMatrix()\n",
      "#ia_solver = ia.MMSEIASolver(multiUserChannel)\n",
      "ia_solver = ia.MaxSinrIASolver(multiUserChannel)\n",
      "#ia_solver = ia.AlternatingMinIASolver(multiUserChannel)\n",
      "ia_solver.max_iterations = 500\n",
      "\n",
      "modulator = modulators.PSK(4)\n",
      "\n",
      "include_path_loss_bool = True\n",
      "\n",
      "M = 4\n",
      "NSymbs = 100\n",
      "K = 19\n",
      "Nr = 4\n",
      "Nt = 8\n",
      "Ns = 2\n",
      "SNR = 30.0\n",
      "users_per_cell = 1\n",
      "\n",
      "# Cluter variables\n",
      "cell_radius = 1.0\n",
      "\n",
      "# Dependent parameters\n",
      "#noise_var = 1 / dB2Linear(SNR)\n",
      "noise_var = dBm2Linear(-116.4)\n",
      "\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxx Create the cluster of cells xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "cluster = Cluster(cell_radius=cell_radius, num_cells=19)\n",
      "cluster.create_wrap_around_cells(include_users_bool=True)\n",
      "cluster.add_random_users(cell_ids=range(1,20), num_users=users_per_cell)\n",
      "#cluster.add_border_users(cell_ids=range(1,20), angles=0, ratios=0.01)\n",
      "cluster.figsize = (7,7)\n",
      "cluster.fill_face_bool = True\n",
      "cluster.fill_opacity = 0.3\n",
      "cluster\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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58uXExMSU6fiC8CoSBV4QXhAbG8vSpUuZPXt2kfvY26dy7Zq6eJ05U41Ro/4hOVmblBQt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YHUVE327y/sZe3gkIKfXw169KjH48daBZOHV2nevDmdOnZkkADHGY5SKXXNzPheGSkyXzJYlYf2\nwaNRNHtnnSRiwkWGhrRCmYttXR0dAocOZeGvv/JUxdEZM6OiyM7KIkzZDln29go/BYmEuXH9memi\n/AQ1m729OXLrFofPnFF62y9z+vp1Dl24UGTbuSTv19hmY/nvUVVCQu4T4VeJlnKHMr1fFhZ5tGhR\nOIQyK0uCg0MKwcH3Wb78LlFR1bh2rbBPgoeHB1paWiov0Zqfn49UKiU4OFhtEv+8T1Rk8FcDJBIJ\nCxcupF27dvz8888qk5ORkUFeXt5b60BraEDjxjnExFTCxCQfHR3o1CmdQ4eqEBeny/ffKwZdI6N8\nDA0VFqpGjTyWLDFGV1dORoYGVasqrltZ5WJqmg9Aq1ZZxMcXHkg8PWuxbt0drKxyWb++GlKpKTNm\nJBerV0BgIB06dKDWsGHo/f92p7KRyWTcffKEswsWKKdBe/uC/9mwx8c0+EYCyPjYPANjg1zup+hR\n21h5Mexj+vVj9tat1HN0xEiFccSJjx8jHTAAfRVFYDxN1yLuXmW6NH+s9LabWVrSpUULvvHzo6YK\nq90lpabSqmVLPvvss0LXhXy/ikNfX86wYSno6srR1YX27TO5dk2PRo1elCDW1NTEx8eHKVOmcOzY\nMaX0R3E8e/aMGjVq0L9/f5XJ+JCpMPBqwj///EPNmjXppsIc3snJyURHR3Pv3r23JraxsckkIsKY\nnj1TAfj88yzCw03Q1JRjYKAYXF7evfbzq0Fw8H3q188lLMyYxESFAb59W5u0NMWAFBurR9++qYXk\nZGVpULmyoj1T0zxiY18/i7958yZyuRz7Vq0wUlHFu7y8PEL27uXE1at8Vr++UtteccCSC/FVCR93\niXuPdUnN1KKWkXIT1KRnZvIoM5MejRrRTMn6v8yigweJVWGBkqOXjfny00cqaz8+KYlG1avTq107\nlcnYFhvLvXv3ir0n1PtVHP/+q4ObWy22bbtNfr7CD6C45/766y8qV65Mjx49SvvVS0xaWhqHDh0i\nIyODKsrIoVBBISoMvBqQmZmJh4cH/v7+tG7dWqWybt68ibOzM9u3b3/j56ytM5gxoyZBQYoYc21t\nMDTMp0mT4p3revVKxcXFnFq1ntG8eTYPHyp+Wrq6cry8zHj8WItWrbLo0qVw7ek5c5JwcTFHR0eO\njo6c2bOTXquTt7c3tp9+SqiqPXwlEjxXrGB09+5KrYg2slsCjgs/o7O3ItHJCudzKDvc12H+fGpV\nrsxmf/8yOWWVlI5t2vDNzJlM/fdfGqugIlrcvcpYmb2h2FA5WLF/P3eSk0lYsYKa1aurRAaAW2Ym\npsOGsXLlSkaMGFHonlDv18s8/zlYWeXSu3cqgwZZoqUlp2/f/7Cyyi302fv37xMdHU1ISAhfqSgF\n9HMyMzORSqVqUXznfaMiDl4NmDVrFqdOnRKkctLt27f55ptvCAsLw9bWVuXyevasR3R0fLnbWbt2\nLQFSKcmrV1NNxTN9mUyG+eDBfNOhA784O6tUljK5nJDAZ05O7J02Dds2bVQur52bG2RkcDIiQuWy\nlEVeXh7GQ4YwplMngpycVC5v2po1zNu5kxMnTqgkoZSy3q9Xsbe359mzZ+zYsUPpbb9KUlISffv2\n5e+//6ZevXoql/chUeFkJzJ37twhNDQUNzc3QeTVrVuXr7/+mqlTpwoSTqWMRWROTg7z5s1jUs+e\nKjfuoAinWjRmDGsPHODuI9VtEyubAVIpnT/6SBDjDrDVx4fY+/fZ9nIqYTXHadkydORy/MePF0Se\n79ChVNLR4aefflJJ+6rYpImJieHy5cuEhIQov/FiMDMzY/jw4YKNgR8SFQZeZDw9PRk8eDC1a9cW\nTOacOXPIzs5mkQBZz377Lb7cbfj6+qKrqYlUWalQS0D/L7/kUzMz+gsUTlVenqdCXamsmPQSYGFi\nwlBbWyYuXSpY7H15SEpJIXLfPkJHjVLq0cub0NDQ4Jcff2Tnzp0kJCgv3O85yni/XsXHxwc7OztB\nS7U6Ojpy6tQpDqtZlb13nQoDLyLHjh3j6NGjjBw5UlC5enp6ODs78/PPP5OR8fozO3UgMTGRHTt2\nsGz8eMHzU6/y8uLs9escOHdOULmlRSaTMX7xYsZ27kwdMzNBZS+fOJEMmYyZy5YJKrcs9JdKaWZi\nwmAVVh4sjl7t2tHM0vKdWKEuWbKEtLQ0/Pz8BJWrp6eHu7s7zs7O5OfnCyr7fabCwIuETCbDycmJ\nyZMnU6lSJcHlOzo6YmxsjLu7u+CyS4O7mxuNLCzor4xUqKWkaf36DPjsM4aLWKmtJHisWAF5eYQI\ncKb8KlpaWkgdHQnas4eU1Ld7cIvFn5cv89eVK6zy9BRF/jZvb/65fFmtV6iZmZlEREQwfvx4UcYk\nOzs79PX1iYyMFFz2+0qFgReJVatWoaGhwbfffiuaDv7+/hw5coS4uDjRdHgTf/75J+cvXGCLSIMy\nwBIPD56mpiLdskU0Hd7Ek9RUFkVHE+jgIGgBlZeZ8M03mBkbM1KNjzOGBAfTt0ULWjRsKIp8q1q1\n6Gdjw7Rp09T2OMPb2xtDQ0PGjBkjinyJRIK3tzfTp08XtWTt+0SFgReB1NRUpkyZgre3t0pDmd5G\n+/btadWqFS4uLqLp8DpkMhlTpkyhV7t2NFFmKtRSYlClCjP69ME3Kors3Ny3PyAwA4KCsDIyYlSf\nPqLqsd7Dg+jLl4kVIJVwaVnw6688SklhuYgTRYDVkyaRnpbGkiVLRNWjOP7991/279+Pn5+fqKVa\nmzZtSteuXVWeQe9DocLAi8CcOXPo2LGj4OUYiyM4OJiEhASio6PFVqUQy5cv57+nT1kncFWr4vAY\nNgxTXV2126o/HRfH4fPnWakGZ7vWjRvTsVkzhqtZNbDc3FymrVmDz7ffUs3A4O0PqBA9HR18Bw9m\n6dKlpKs4lXBpcXZ2pkWLFnTq1ElsVXB2dmbNmjVcVcPJ4rtGhYEXmOvXrxMZGak2q2YzMzPs7e3x\n9fVVm63DzMxMFi9ezPQBA6ikolSopUFDQ4PlTk5sP36c64mJYqtTwKDAQL5p0oS2zZuLrQqgyPEe\n9+QJq3fuFFuVAhzDwjDU0mLqDz+IrQoAHv36YVy1Kj4CRju8jb1793Lr1i3BwuLehrGxMaNHj2bS\npEliq/LOU2HgBcbV1RVHR0dMTU3FVqWA5zHxr6sPLzRTp07FsFIlpg4oe/1rZdPd2hprS0u1CZtb\ntncviY8eEalGhsLEwICx33zD5JUryVWD44zbDx6w6ehRlogQgfEm1k6ezIGDB7l27ZrYqiCTyZgx\nY0ZBwSt1YciQIVy/fp3du3eLrco7jfr86j8A/vjjDy5evMj3AsZzlwRtbW08PT2JioriyZMnoupy\n8+ZN9u7dy2o12eF4mdXe3lxJSGD7iROi6pGXl4f7zz8zuVs3TFVYLKUsLBg1CpmmJt6LF4utCt9J\npbSuXZteXbqIrUohbFu0oG3DhnioQQTLvHnzePbsmcoS8ZQVHR0dPD09mTRpklpMFt9VKgy8QOTl\n5eHi4oKnp2e5vZ3lcjkh50JQZpbhgQMHUrt2bSaLfObt7uZGSysr7Fq1KtkDcjmsWkWRQtgqoF7t\n2jhaWzMmLEzU44zxERHoSyTMHjtWNB1eh4aGBmHjxhF+6BD3Hj4UTY/dZ85w/tYtVqvRDsfLbPXy\n4uatW+wU8TgjNTWVVatW4erqiq6urmh6vI4uXbpgbm4uSEKu95UKAy8QS5YsoXr16krJ/77vzj7W\n31jPvrv7lKDZCwIDAzl9+jTnz59XarslZc+ePcTFxbGtNIPy8eOwe7firwAsmDSJ3Jwcpq9dK4i8\nV7n3+DGr/viDsNGjBcvGVlocunalvrk5jiIe+fywYAEObdrwsaWl6oWVYZJpbmzMCFtbZs+eLVpi\nF1dXV2rWrImDg4Mo8t+GRCLB09OTuXPnkpxcfAnpCt5MhYEXgMePH+Pr64uXl1e5wuI23thIj909\nmH9hPhl5Gcw/P58eu3uw8cZGpej56aef0qFDB1xdXZXSXmmQyWTMmjWLoV26YFkS/4Q9e2DiRFiz\nBrKyFH8nTlRcVyH6enpIBw8mZPt2UjNVU+3sTfSXSvmkZk0GqLCssDLY7OXFwZs3ifn7b8Flz4qK\nIiMzk0VC/Y7LOMmMGD+evNxcgoODVaTY67l8+TLHjx8nICBAcNmlwcrKip49ezJt2jSxVXknqTDw\nAjBjxgy+/vprGhaTZMPR0YKLFxWe4rm50Lp1AyIjX5yrDhtWh2vXFNtnA6wG4NTciex8RQ3x7Pxs\nnJs7M8CqZM5orq61GD68DsOH18HWtj5ubrWKfCYwMJDk5GSioqJK/T3Lw/z588nJymL5xIkle8DO\nDgYPVnQaKP4OGQJ2dpy8Vo2uU60BiL1pgIXj/+g61ZquU63Z9GfR71xaxvfvj2XVqgwKCip3W6Xh\n4IULnI6LU3o2Nv/NDbDx7EAb106sOmChlDab16uH3eef88OCBUppr6SkZ2Yi3byZOfb2VFZCNraX\nf0vn/zXA2qMDnbxt+GHhp+Tu+qNck0wtLS3m/fADa9esEdz3ZfLkybRr147PP/8cKN04pCz8/U3Z\nsMGw4P8fPVqZgQMtGTjQkjlzahRcHz9+PNu3b+ecmqeMVkcqDLyKuXTpEhs3bmTChAnF3rexyeTM\nGX0Azp6tRKdOGRw9WhmAnBwJ9+9r0aiRoka0RCJBgoS0Z2lYGViR9iwNJJR4V2DevPusXn2HRYsS\nMTTMx8en6LaXkZERw4YNIygoiLy8vLJ85VLz9OlTVq5cScDw4SX3T5BIFP9lZECdOoq/QOC2Boxe\n9Ck5zxQ/7bM3q+Ha+xaH/E5wyO8EAzreV4rOv0yezL6//+bCv/8qpb2SMHzePL779FOaN2igtDYP\nXzTmxDUjjgce4/Dc49x6oLwUpRvc3bmTlsaiTZuU1ubbcJg/n1qVKuEyeHC52wrcalXotzQq7FPm\nj7xMjPQ4tY2zWZw35rWTzJIyxs6O2iYmeHp4lFvfkrJt2zbu3btH0EsT1NKMQ+XlyRNNRo+uzaFD\nVQqq4aWnSwgKMmXp0rts3JhAzZp5PHmiCYChoSE//vgjzs7OSvU7+hCoMPAqRC6X4+zszIQJEzB6\njbezjU0GZ88qXqyjRyvTv/9/pKVpkp6uQWysHm3bZgFga1ufUaMsWBXaAqeaEZhtPUftrf8wZ/R3\nxMYqZt69e9fF2dkce3tLpk+v+dojwdBQExwcUjAxKf7sz93dHR0Vlrh8FW8vL8yMjPixtGl7790D\nZ2dYtEjx9/59GtTKYJvP6YLvfvaGIbvO1KCLjw2jwlqQnqWpFJ07tmzJ/xo0wF6gLc6gbdt48vQp\ny5S8et8Xa8onddPo49eGnrPb0qvtA6W1XaVSJTy++46pGzaQlZ2ttHZfx+WEBHadOsXPSorAePW3\ndPexHu0bK1Ko2jRJ4chl42InmaWt4brJ3Z3jJ04I4vuSl5eHn58fgwYNwsTEpOB6acchf39Trl/X\nYeRICxwdLejTp26Jx6HMTAlOTo/p1Su14F5srD4NG+YgldbAwaEOpqZ5VK/+Ynyyt7fn0aNHbN26\nVYW98/5RYeBVyG+//UZiYiIDBw587WeaNMnh1i3FqvXMGX3atMnE2jqT48crcfq0YiYNkJSkRUjI\nPdYH16Jmui1eXg/5LSoLrx8lbNum2OZKTNRm+vQHbN6cQGqqJvv3F62d/vixJidPVqJfv9cXBtHQ\n0GD69Ons2LGDpKSk8nTBW7l06RJHY2KIKks2Nnt76NBBMaB26AD9+9PPJgktzRcjSrtGKQQ7/sMR\n/+PUr5nJrA3Ky0W+0seH+Pv3WaXieujZubnMXLeOab17Y1Cl6L9peXiYqsPZG4Zs8T5DxIQLDA0p\nYfRCCZk1ZAi6+vo4C5AFcIBUSuePPsK2TRultPfqb6l+zUyOXqoOQPSpmmRkaxU7ySwtbRo2pEvz\n5oKs4ufMmYOmpiY+Pj6Frpd2HPLxeciNG7p4eSWzYsVdRo16UuJxyMIijxYtClk0bOwAACAASURB\nVE/4nj7V5NSpSnh4PGTZsrusXm1EfLx2wX1NTU28vLxwc3MjKytL6f3yvqKebrjvATKZDFdXV6ZO\nnfpGb2cNDWjcOIeYmEqYmOSjowOdOqVz6FAV4uJ0+f77FACMjPIxNFSEZtWokceSJcbo6srJyNCg\nalXFdSurXExNFbPeVq2yiI8vut29d29Vvv029a2LjB49erBkyRL69etHkyZNytIFJeLypUu0b9SI\nDiqS0bd9EoaVFUcNfdon4bxceVnfzExMmNC1K2PDwli+d6/S2n2Vf5OSMNDSwlsF+RNMqubSxCId\nLU05DWtnoKeTz6NUHUwMlBN7rKGhwc/OzvTz8+OSuzuamsrZQXmVtMxMrt+9yx4Vlq1d4XIOl+XN\n8d0ooVPTJzzN0FJMMp/ToUOZ297i6UnN77+nf//+GBoavv2B1yBHzu0Gt6l7oy4SCr/kMpmMkydP\nMnfu3CL/DkKPQ69SrVo+zZtnY2yseK5160yuXtWlXr1nBZ9p3749TZs2ZeHChXirafijulFh4FWI\npqZmic6xbWwyiYgwpmdPxar688+zCA83QVNTjoGB4qV5ORGXn18NgoPvU79+LmFhxiQmKma6t29r\nk5ameNFiY/Xo27foKv3EiUpMmPC4RPo/D99RZcy3TC4nT4VhQl/NbEfomEu0+fg/DlwwoXUD5Vap\nepCXRyUNDWpra7/9w2UkS0uLmyo6e+zY9AkLo+vj2ucW9x7rkpGtiXFV5SYWyX32DA2gto4OmirK\nKJeio8M/oNKQs52na7LO7W+qV32G87Jm2LVUXpx/nkyGXC5HJpOV6317XOMxSXWSqPy0MsbJxoXu\nyWQyJBLJaxPHCDkOvUrTpjlcv65DSoriufPn9Rkw4L8in8vLy1PZJPF9pMLAqwgNDQ3mz5+Pi4sL\nNjY2aL/BAFhbZzBjRk2CghTbe9raYGiYT5MmxTu19OqViouLObVqPaN582wePlT8M+rqyvHyMuPx\nYy1atcqiS5eMIs/Gx+tQp86zItdfZceOHSQkJHD48GGVptW9du0affv25fDFi3yhxOI7z3coIiZc\nZGLEJ2hryahllM2yiReUJuP2gwdsjolhu5sb33burLR2XyU3NxezIUOYHBnJwtGjldp2jzbJHL1s\nTFu3jsjkEhaPu1jaI+Q3IpPJGBcezuiOHQlXcTW3Fk5OfC+VciQsTKntPu+PhrXT+d90a3S182nb\n8CnDbe8qTUb/gADq1q3Ltm3byvT8xhsbWR23mnxZPvnp+eR2yiVZI5nhDYczsMGLI0I/Pz8CAwPp\n27dvkTFJyHHoOc/71tg4H1fXR4wapagc+c03qTRoUHgicuzYMW7evImzs3PpOucDRiKvcEtUGXK5\nnK+++orPP/9ckPS0PXvWIzo6vtzt5OfnY21tzddffy1I2caJEydy5fx5En/5ReWylEnryZPRzsnh\nhABpWVdGRzM2MpLE1asxEbkqWmnwWrmSpdHRPIiKQlfF9eqvJybSdMIEdnl7093aWqWylMmxK1fo\n7OPDpk2baF7GwkFyuZy9d/YijZWSlJWEmb4ZPi196F6ne6EoG5lMRrt27ejRowczZ85U0jcojLLG\noZfJy8ujX79+BAQE0Efk0sjvEhVOdipEIpGwYMECli1bJkicq7JWXgEBAeTn5zN9+nTlNPgWpFIp\nj1JTCdm+XRB5yuB5KtQ1Ap0FjujZk4+rV2eAmicmeZmn6eks/PVXAocOVblxB/i4dm16W1szWuRU\nwqVlcEgInTt1KrNxh5KH0GpoaDBt2jQ2b97MgwfKi5gorIvy29y4cSO1a9emd+/eym/8PabCwKuY\nJk2aMHToUMKUvG1YHL/9Fl/uNh49esT69evx8fERLBVq1apVGT16NNPXryf7HSks8TwVagMhUqH+\nPyvc3Dh68SIn1aAKWUkYGBTER4aGjOnXTzCZqydN4mFODsEipRIuLWHR0SSlpCBVwsQtPi2eue3m\nsvPrncxtN5fbabeL/Vzv3r2xtLTErSyRKyVAGePQy6SkpLB48WJCQ0PLlQn0Q6Rii14AUlJSaNy4\nMUuXLlWpR7oycHR0JDExkX37lJvn/m3IZDI6d+rEl02bskHApB9lYVZUFMGbN5O0dq1SsqWVhj7e\n3px/+JB/IyMFlVta/r5xg3Zubvw5Zw7tlOhbURKkW7Ywe906HqxbRxWB/31KQ25uLkYODjgMHy54\neui4uDj69OnDL7/8Qvv27QWVXVrmzJmDgYEB4eHhYqvyzlGxghcAIyMjfH19kUqlap2J6ezZs5w8\neZL5AsQsv4qGhga+s2ez5dgxbpYhllgolJ0KtbREenuT9Pgxi9W8TvbAwEDsGjYU3LgDePfvj2HV\nqowNDBRcdmkYFR6Onr4+kyZNElx2w4YN6dy5c5F4eHXj2rVr7NmzB19fX7FVeSepMPACMWrUKDIy\nMtirwnjp8uLl5YW1tTXNmjUTRb6trS1NmjThOzU+Zx46b57SUqGWBeNq1XC1s8NrxYoXIZgClswt\nCZF//MGdBw+IFNF4rJo8mU2xsfxz65ZoOryJhIcPWX/kCD/NnImGikIH30ZISAiPHj1ixYoVosh/\nG3K5nMDAQKZPn46xsfHbH6igCBUGXiA0NTUJDQ0lJCSEbAHSdpaWtWvXkpSUJMrq/WVC5s3jUnw8\nv508KaoexXE5IYHdp08TqaRUqGVl9tixVNbQYMxz732BS+a+iby8PFyXL8elWzdqVq8umh7dWrak\nVYMGjFDTyWI/qZSGDRtiV4q89cqmcuXKjBw5ktDQULUckw4ePMiTJ08YP3682Kq8s1QYeAHp2rUr\nrVu3ZtWqVWKrUoicnBzmzZvH999/j4HIIVh169alR48e/LBokdp5Qg+QSuny0Ud0VVIq1LKioaHB\nojFj0N2/n2fjxgleMvdNOC1bhq5cjt+4caLp8JytPj7EJiWx5cABsVUpxJ6zZ4m9eZPgkBCxVcHZ\n2Rk9PT1mzJghtiqFyM3NJTAwkIULF74xh0gFb6bCwAtMSEgIq1atUlmISlnw9fVFW1tbZV61pcV3\n9mwyc3KYKXDJ2jex8ehRricmskJNUmT2//JLzpiZMUMmK1c1M2WSlJJC5L59LBw1SrAIjDdhYWKC\ng60tPy5bplaTxRGhodjZ2WFlZSW2KmhoaODn58euXbu4fbt4r3sxWLVqFZ988gndunUTW5V3mgoD\nLzAfffQR48aNY968eWKrAkBiYiI7duzA19dXtLPAV9HT08PZxYWAbdtIz8wUWx1kMhnjFy9mbOfO\n1DEzE1udAlZ5e3MzKYm8tLRyVTNTFv2lUpqZmjJYxG3nV1k+cSIZMhk/qTBHfWmYvXEj/2VmMnfu\nXLFVKcDW1paPP/4Yd3d3sVUBIDk5mRUrVoh+XPg+oB4j+geGj48Pp0+f5ty5c2KrgpubG/Xr1xf1\nLLA4HB0dMTExYZAabGO6//IL5OUR4uQktiqFaFq/Pv1q1WKcpma5qpkpg6OXLnHy6lXWeHmJIv91\naGlpEejoSPCePTx+qtw6BKUlMzsbv82bmTBxIvr6+qLq8iphYWFcvnyZgyqujFgSFi5cyMiRI2nQ\noIHYqrzzVBh4EahSpQpSqRSpVCrq1uGff/7JhQsXCA0NFU2HNzHX35/fz57lUny8aDo8SU0lfOdO\ngocNQ0eAbGyl5dv584nKyUG6dWtByVwxcAgJoW+LFjRXw0F5/DffYGZszCiRHe6Gzp9PtWrVGDVq\nlKh6FEedOnWws7NjxowZoo5JFy9e5NixY0ybNk00Hd4nKgy8SAwdOhRtbW2io6NFkS+TyZgyZQq2\ntrZ89NFHKpcnl8sJORdSqjwA7du3p1XLlvQXMZ55QFAQVkZG/KCmKTKrVKrEjD598I2KEi0L4IJf\nf+VRSgrL1DhB0XoPD6IvX+bslSuiyL969y6/nTyJ39y5anMU9ir+/v6kpaWJllBGLpfj7++Pn5+f\n6M6+7wvq+Uv7ANDQ0CAsLIz58+eTnp4uuPzly5fz9OlTAgUynvvu7GP9jfXsu1u6DHnBISHcuH+f\ndYcPq0axN3A6Lo7D58+zUk2cD1+Hx7BhmOrqMkwEv47c3FymrVnDlJ49qabGg7J148Z0at6cEUFB\nosj/LiCAT1u0oGPHjqLILwl6eno4OzuzfPlyUcaknTt3IpfLGTFihOCy31cqDLyItGvXjv/9738s\nX75cULkZGRksXryY8ePHU0nF2dg23thIj909mH9hPhl5Gcw/P58eu3uw8cbGEj1vZmaGvb09E5Yu\nFXzrcFBgIN80aULbchQBEQINDQ2WOzmx48QJricmCirbMSyMatraTHF0FFRuWdjs7U3ckyesFHjX\nbPOff3Lt7l1C1MSx9k2MHDkSIyMjvAWOFsnMzGTevHmEhYWp7Q7Hu0hFT4pMQEAAmzdv5s6dO4LJ\nnDp1KlWrVi2UQMLR0YKLF/UARbRV69YNiIw0Krg/bFgdrl3TLbWsAVYDcGruRHa+IpFGdn42zs2d\nGWA1AH9/UzZsMCz47IoVRvTrV5dBgyzZubNqIX1lEgmTBcy/vmzPHhIfPSJSSQPdyWvV6DpVUcI0\n+akOvee0oYuPDZ29bYh/UH6Hq+7W1thYWvKdVFrutkpKfFISm44eJWL8eKUMyi/30dW7Vejo1YFO\n3jaMDP1UKUn6TAwMGNejB26rVpEr0HGGTCZj7JIl9O3TB3Nz80L3VPXOvYlX37n8fHB2Nicm5sVE\nPygoiIMHD3KtBEWNynL0VhyRkZF06dKFDh06lKudCgpTYeBFxtzcHFdXV4KDgwWRd/PmTfbt24e/\nv3+h6zY2mZw5ozA0Z89WolOnDI4erQxATo6E+/e1aNQop9Tyiitjmf6fPmPGWHDoUJWCiK64OB1+\n/dWADRtus3r1HZYuNebRI00AtLW18fT0ZPHvv/MoNbUc375k5OXl4R4ZyeTu3TE1Mnr7A28hcKsV\noxd9Ss4zxevmubIpw7re5Yj/cXyHXuNSQtW3tFAyVnl7czUhge0nTiilvbfxnVRKm9q1+bZz53K3\n9WofzYxqyLQBccRIj5PzTINdZ2qUWwbA/JEjkWlq4inQObP3qlXk5OczvZhEMqp654rjyRNNRo+u\nXeidS0jQxsHBkkuX9ApFVrZt25YWLVqUKGyurEdvL5OYmMj69esFOy78kKgw8GqAm5sb165d46+/\n/lK5LHd3d5o2bUqnTp0KXbexyeDsWcVgc/RoZfr3/4+0NE3S0zWIjdWjbdssAGxt6zNqlAX+/qZc\nv67DyJEWODpa0KdPXWJjFauR3r3r4uxsjr29JdOn1+Tf1MJlLG89foCT02N69UotWJnduqVD27aZ\n6OiAjo6cjz/O4fz5FyvbAQMGYFG7NvYCDALjIyLQl0iYPWaMUtprUCuDbT6nC77r8atG3HmkT7fp\n7Vl3pDa2LR4rRU692rVxtLZmjAD10HefOcOFf/9ljZLyzb/aR/o6+TxO00Euh7QsLXS0lJNnX0ND\ng7Bx41hy+DD3Hj5USpuv42l6Oguio3F1dUVXt+hKXJXv3KsL6sxMSZF3LitLAz+/JNq1yyzy+YUL\nF3Lr1q3XOgGX9+jtZUJCQnB2dqZOnTqlfraCNyN+uqkK0NPTIyQkhEmTJtGlSxeVyXn06BFxcXHs\n37+/yL0mTXK4dUsRBnbmjD6TJz/E2jqT48crce2aLp06KZKoJCVpsX17PIaGMn7/vSpeXsk0bJjL\nzp1V2bbNkJYts0lM1Obnn+9iapqPi4s5ne+70a25wmnHro4ddnUAsgtWKwANG+aybJkxGRkScnMl\nxMbqY2tb2NEnIDCQQYMGMSAgAINy+A7IkXOm7g1a326AhMJJYXKfPWP94cOsd3JSWja2fjZJhbbh\n45MrUb1KLn/M/ovZGz4mYKsVs4bEKUXWgkmTWD9kCNYeHnxSr55S2iyOLTExDGvTBislDcqv9pHT\nt/F0n9GeOZs+plrlZ3RprpxJEIBD167M3riRTh4edP38c6W1+yoHL1ygppkZDg4Oxd5X5Tu3f38V\nunV78f5YWORhYZFX6J170+5AzZo16devHzNnzuT8+fNF7suRY17ZnLMmZ0EbklOS+fzR58T9Hccc\n5pS4j9LT0zl37hybN28u8TMVlJwKA68mfPTRRzx+/JiYmBiVycjIyEBDQ6PYykwaGtC4cQ4xMZUw\nMclHRwc6dUrn0KEqxMXp8v33KQAYGeVjaKhYHdaokceSJcbo6srJyNCgalXFdSurXExN8wFo1SqL\n+Pi3x4/Xr5/L0KEpjB5tQa1aebRokY2RUX6hz5ibmyORSDh19y665YhJT6uTRlKNBzy8mU3Vu4W3\nx/NlMmQSCR/VrVvm9t+GcdVcerVTpCru2fYBU9c2Vlrb+np66Onqci8xkTwVekJn5eTQSIUrLod5\nLYmRHqNJnXQW766LW2RTFo27pLT2axsbc/LGDWJUmGMhKTWVxo1f/28r9jv3Nj755BO2bNny2jEp\no24GWR2z0ErRIqtyFleuXCHhdkKpZOTm5lKtWjW1S/zzvlBh4NUAuVyOs7Mz3t7eDBgwQGVyZDIZ\nHTt2ZMqUKcWe+dvYZBIRYUzPnopz7s8/zyI83ARNTTkGBoqB5GVfKj+/GgQH36d+/VzCwoxJTFQU\nhbh9W5u0NMXgExurR9++bz83T0nRJD1dk/Xr75CWpsHgwZZ8+mlWoc84OztTv359du7cWabvv/HG\nRlbHrUZXpoc8XY5uDz3yNWQMbzicgQ0GFnxu5MiRDAgO5l8VpTft2OQJu07XwKFrIkcuGdPcMk1p\nbftu2EB2djbx69aptF6936ZNzI6K4kd7e5XIyczRpKq+ohxuLaMcjl8ppjKdXA6rV8Pw4aVKz3vu\n1i2OXL7M6tWrad26tbJULsKVK1f47rvvOHXqFG3bti32M2K+c28iPz+foKAgBg8e/NpCNEsvL6We\nQT26W3Rn39193G59mzFNS3esJZPJGDhwIFFRUQwZMqRcOldQlIozeDVgy5YtPHnyhO+++06lcjQ0\nNPD19eX3338nsZhwKmvrDGJj9encWbE1qK0Nhob5tGmTVeSzAL16peLiYs6YMbWRy+HhQ8V8UVdX\njpeXGQMHWlKrVh5dumS8Vqfn47KRUT7//quDvb0lo0ZZ4O7+kCpVXhwMnj59mvPnz5fLGfFNHv0v\nExQUROKjRyzbu7fMsorj+XcNGfkPqw/VoYNnB/adM2WK/XWltJ+ZnY100yZm9e+vUuMOMHXAAKpW\nqcIEJTuHPu+jn388T39pa76YYk3EnrrMHX616IfLWCbXPjCQNm3aqNS4AzRp0oQOHTrg8wY/BTHf\nuTddmzt3LnK5/I0Z5cY2G4tdHTskEgl2dexKbdxBMSb5+Pjg6elJRsbrda6gbEjk5Y1vqKBcZGVl\n0bhxY+bMmfPaWb6y6devHxKJhK1bt6qk/Z496xEdHa/UNrt06UKzZs1Y/LwGehnZm7CXKaemYFbJ\njKTMJOa2m4tdnaJ5+KVSKZs3buTpunVqURmtJPSbO5fYy5e5uWaNILHEe//+m29nzeJSaCiNVHik\nUYQ9eyA6WhHjde8emJuDpib07AlfffXGR1cdOMCo8HAOHjxIjRrK8cx/E+np6VhbW+Ph4cHw4cNV\nJkeZ71xycjK2trbMmTOHPn36KKXNt+Hu7k7Lli3x9fUVRN6HQsUKXmSCg4Np1qyZYMYdFF6rV69e\n5ciRIyppX9nFzFasWEFKSgpSJcR4x6cV9ui/nVZ8iUwPDw+0dHQYHxFRbplCcOXOHaJPnmS5s7Ng\niULsWrXiMysrvhcw9l4h2A4GDy51mVyZTIbT8uUMHDhQEOMOiroTjo6OLFiwgJwc5YS8FYcy3zkP\nDw8sLCwEM+4Arq6uLFq0SK1K1r4PVKzgReTu3bt8+umnbNq0CQsLC0Flu7m58ddff3Hs2DFB5ZaW\n7Oxs2rdvzw8//ICzs7OgsqOjo/H29ibh558xL8YxUZ1oPnEiplpaHFq4UFC5CQ8fYjVqFFsmT6b3\nF18IJ/jYMQgNBRMTePRIUUnvLUlSJkZEsCYmhr/++gtNTU2BFFVMLGxsbPjiiy+UMklVJadOnWLE\niBFs3bqVJk2aCCo7PDyc+/fvV3jUK5GKFbyIeHp6MmjQIMGNO8CcOXPIyMhg0aJFgssuDVOnTqVy\n5cr8+OOPgsvu2bMnVvXr853IVcjexqY//yTu7l1WKykmvTRYmpoy5IsvGB8RIWwq4Xv3FEa9hGVy\nk58+Zdnevfj4+Ahq3EFxzjx79myio6MFzVhZFnx8fOjQoYPgxh3ghx9+4MSJExw9elRw2e8rFQZe\nJI4fP87hw4dFKx2pr6+Pk5MTy5YtU1vnljt37rBnzx5mzZolWn7qoOBgTsXFcejCBVHkvw2ZTMb4\n8HBGd+xIHTMzUXRYPnEiaXl5+AqYShh7e8WKXSIpUZnc/gEBWNatS79+/QRSsDDdunXDysqqRNnh\nxGLVqlUkJyczf/58UeTr6+vj5uaGk5MT+fn5b3+ggrdSYeBFQCaT4eTkxOTJk1Ve7OVNPC8s4aGm\nZT6dnZ1p3Lgx//vf/0TToVGjRth27cpQkQa9t+G1ciX5ubnMc3ERTQcdHR38R4wgYNcuUgRIJVxa\njl25wrErVwgSqZLcc0JDQ7l48aLKfF/KQ05ODgsXLsTR0ZEqVaqIpsfXX3+Nrq4uv/zyi2g6vE9U\nGHgRWLNmDXK5nG+//VZsVZg7dy6HDx/mxo0bYqtSiEOHDhEXF8c8NajAJQ0I4HFaGkHbtomtSiGe\npqcT+ttvBA4dWq7EP8rgx2+/pUb16oxWw+OMwSEhdO7UiWbNmomqR7169ejWrRvTpk0TvDLi2/jp\np5/Q0dFh0qRJouohkUjw8vJi2rRp/Pfff6Lq8j5QYeAFJi0tDR8fH3x8fNSiLGKHDh347LPPcBFx\nBfgqcrmcqVOn8vXXX1NXyPCr11ClShVGjx7NjKgosgWqQlYSBgQGUr9aNcaItO38Kuvd3Wlz8SLn\nrhYTsy4SYdHRJKWkIFWTiYdUKiU1NZWlS5eKrUoBd+7cITo6mtmzZ6vFmNS8eXO6dOlSETKnBMT/\n1/zA8PPzw9ramhYtWoitSgHBwcHEx8eze/dusVUBYNGiRWRmZjJ79myxVSlg4sSJVKlalRECe6m/\njjM3bnDw3DlWurmJrUoBHVJSmKihweo5Jc9Frkpyc3PxXrMGR0dHqlWrJrY6gOKceeLEiURERKiN\n74u7uztWVlZ069ZNbFUKcHFxYeXKlSUqWVvB66kw8AJy8+ZNli9fLvo22KuYm5sXFJYQe+swIyOD\n5cuX4+TkpFb5qZ9nAdxy7Bg33+KxLQSDAgL4qlEj2n3yidiqKBLPTJwIa9ZQRSZjXGoqT7//XnFd\nREaFh6Onr69279uYMWMwMDB4Y4Y7oTh8+DAXL14kNDRUbFUKYWJiwqhRo5g8ebLYqrzTVBh4AXF1\ndcXR0VGwJBulYcaMGeTn54tek9nd3R1jY2NGjhwpqh7FYWtrS9NmzfhO5Fjmn/fu5W5yMpFqYCCA\nIolnTPT0mJSWRt6XX4qmUsLDh6w/coSfZs5Ui23nVwkICGD//v1cv66cNMVlQSaTMX36dLp37049\nFVYeLCsODg5cvXqVPSJPFN9l1O+X/55y4MABzp07p9J0leVBW1sbDw8P1q5dy9OnT0XR4caNGxw5\ncgR/f39R5JeEkJAQLt2+zW8nT4oiPy8vD7fISFy6daNm9WIKsIiBRKL4LyMD6tTBSCIhXyLBe8kS\n0VTqJ5XSsGFD7N6S3U4sbGxsaN68uahhc0uXLiU1NVVtk+/o6Ojg4eGBi4sLz549E1udd5IKAy8A\neXl5ODs74+Hhga6urtjqvJZBgwZhbm4u2raYs7MzLVu2pH379qLILwmWlpb0+PZbfli0SJTjjB+X\nLkVXLsdv3DjBZb+RlxLPSJydmdS2LWEHDpD06JHgqvx+9iyxN28SHBIiuOzSEBoayo0bN9i1a5fg\nsjMyMoiIiGDixIno6ekJLr+kdO3alZo1axIeHi62Ku8kFQZeAJYuXUq1atVEjecuKYGBgZw8eZIL\nAid22bVrF7dv3yZEzQdlAF9fXzJzcpgZFSWo3KSUFH7Zt4+Fo0apXwGcVxLPfO7lRb1atXAUYXU4\nIjQUOzs7rKysBJddGszMzOjVqxezZ88WfLLo4+ODgYEBY8aUvgKckDwPm5szZw4PHz4UW513jgoD\nr2KePHnCrFmz8PLyQvL/FSEcHS24eFExa87NhdatGxAZaVTwzLBhdbh2TbkrfX9/UzZsMCz4/1FR\n1ejf3xJ7e0v273+R2OKzzz7D2toaV1dXpcp/EzKZjFmzZtG/f3/MXsrGpg79lJ8Pzs7mxMS8SEik\np6eHy6RJBGzbRlpm5tsblcth1SrF33Lwnb8/zWvUYLCStp1PXqtG16nWAAwKakXXqdZ0nWpNvVFf\nMiS4Zbnb3+zlxf7r1/kzNrbcbZUU3w0bSM3MZO7cuQXX1OF3tHKlEQMHWjJwoCXh4S/qGsyePZvc\n3FxBJ7bXr19n//79BLwUOqgOfbRpkyH9+yv66PDhygXXGzRoQI8ePZg+fbpS5X8IVBh4FfPTTz/R\nvXt3GjVqVHDNxiaTM2cUHuJnz1aiU6cMjh5V/KBzciTcv69Fo0bKqTz15Ikmo0fX5tChKgUVpzIy\nJERGGrFhQwKRkXeZO7ew019QUBBJSUls2LBBKTq8jYCAAPLz84vUnha7nxIStHFwsOTSJb0i1bpG\njBiBiakpg0pSD72Mdctf5vDFi5y6epXVXl5lbuNlArdaMXrRp+Q8UwwBGzz+5pDfCbb7nMao8jPm\nj7xcbhktPvqIbq1a8YNAWQAzs7OZu3kzEyZOLBSBIfbv6M4dbXburMqGDQls3JjAsWOVuHZNkZhI\nS0sLDw8PVq9eTUpKilJ0eBtubm40b94cGxubgmti99HDh5qsXWtEVJRixjFGMgAAIABJREFUTJo3\nz5SXU05MnDiRrVu3cv78eaXo8KFQYeBVyOXLl4mKiipSKMXGJoOzZxUv09Gjlenf/z/S0jRJT9cg\nNlaPtm2zALC1rc+oURb4+5ty/boOI0da4OhoQZ8+dYmNVcy2e/eui7OzOfb2lkyfXrPIIjEzU4KT\n02N69UotuPf8pcrM1CAjQwNNzcIPVa9eHQcHBwIDA8nLy1NyrxTm6dOnrFu3Dk9PT7S1tQvdE7uf\nsrI08PNLol27zGIX3/7+/uz5+28uxccX/+VeCh8jK0vxd+LEMoWPDQsJoe+nn9K8QYNSP1scDWpl\nsM3ndJHvNWN9I5x7/ktNI+Uk9Nng4UFCWhpLtmxRSntvYsi8eVQzMipS30Hs31GtWs/4+ee7Be9d\nXp4EPb0XDw4ePJgaNWrg6empgl4pzK5du7h582aRsDix++jiRT1atcpCWxuqVJFRt25uoR0DQ0ND\nJkyYgLOzMxUFUEuOmh3kvT/I5XJcXFwYN24cRkZGhe41aZLDrVuKGfyZM/pMnvwQa+tMjh+vxLVr\nunTqpEiAkZSkxfbt8Rgayvj996p4eSXTsGEuO3dWZds2Q1q2zCYxUZuff76LqWk+Li7m7N9fhW7d\n0gtkWVjkYWGRVzAbB6hUSU6PHmn06PERMhmMHfu4iP4eHh5s376dkSNHqtQTeePGjZibmzNw4MAi\n98Tup7etWNq1a8fnrVrxta8vPsUVO5HLqd+4MR2OH6cqkPbffxyzseFWfr5iRV9C/rx8mUcpKSxT\nYqxyP5sk4h8UzjOQ/FSHgxdMWDi6/Kv35xhUqoRr3754rl9Pnq6uyiq5paSl8dvJkyxfvrxIWJzY\nvyMtLahWTYZcDoGBpjRtmkPduoW9wkNCQhgyZAgRERGYmpqqpI9AsVvWq1evQkdhIH4fZWRoULXq\niwIzlSrJSE8v/FsZMGAAGzduZPv27aIVDXrXqDDwKkImk3Hq1KliPdI1NKBx4xxiYiphYpKPjg50\n6pTOoUNViIvT5fvvFVt1Rkb5GBoqnG9q1MhjyRJjdHXl//8yKK5bWeViaqp4MVq1yiI+/u05yf/+\nW49z5/Q4cOAmIGHkSAtatszmk0+yCz6jqalJo0aNOHv2rEqzSaWlpdG9e/di74ndTyWhq60twcHB\nTHvNcUbP3FxsMjO5qqGBeUYG648dY+fp06WSkZmVRY3q1almYKAMlV/LluO1GNolschxRHkZYWuL\ndOtWZmzaVOCHomzy8vLQ1NSkdevWRe6pw+8oJ0fClClmVK0q46efHhS536JFC3R0dIiIiCiyk6VM\n0tPTi51Mi91HlSvLyMh4MTHLzNTAwKBwRTktLS3atm1LTExMhYEvIRUGXkVoamoyffp05s2bx+LF\ni4vct7HJJCLCmJ49FdW3Pv88i/BwEzQ15RgYKF6Ulxcifn41CA6+T/36uYSFGZOYqBgEbt/WJi1N\n8XLFxurRt+/bq3llZWmgpydHUZ9EjoFBPmlphVc9V69e5cyZM6xcuZK2bduWrRNKwI4dO5g6dSoP\nHz4sduUiZj+9jWfPnrF48WIcHByYMmVKsZ8xWrqUtHr1kHTvTtq+fQTfvs3UUnou37t3j27durHx\n6FEGdu5cbr1fx4HzpkwfGKf0dvsHBvJpixZs2LhR6W0/RyaT0blzZ3x8fIotdyrm70guhwkTamNt\nncmoUU+K/cz8+fORSCScPHkSHRUWDnJwcMDHx4fff/+9yD0x++iTT7JZsMCU3FwJOTkSbt7U4eOP\nC++gJSQksGvXLi5evFjWr//BUXEGr0JcXFy4ffs2MTExRe5ZW2cQG6tP586KrS9tbTA0zKdNm6xi\n2+rVKxUXF3PGjKmNXA4PHyrmZrq6cry8zBg40JJatfLo0qVwfmu5XE7IuRBAXrAy69AhkwYNchkw\nwJJBgyz56KNcbGwKe4O7u7vTokULlRp3gD59+lCvXr3XFrsRqp9eprhFZnHX/Pz8kEgkeHt7v7at\nlLFjSbezA4mEdDs7UsoQlvQ8lfDYiAilh1O9/L2uJVamvlkJogJKwdbjx/nnzh1CVFwV8Hkq4b17\n93L79u0i98X8He3fX4UzZ/SJianE8OF1GD68DufOvYg9f/r0KStXrsTNzU2lxh1g4cKF3Llzhy3F\n+ESI2UempvkMG5bC0KF1GDGiDpMnP+LVrggODsbNzQ1zc/MyfPMPE4m8wmNBpURHR+Pq6sr27dtV\nsvXWs2c9oqPjX3t/b8Jeppyawtx2c7GrU7Kz9N9++w0fHx8OHjxIzZo1laTp67ly5Qrfffcdq1at\nok2bNiqR8bZ+Ki0PHjzA1tYWqVRKz549ldbu63j27Bnt27dnzJdfMu8VJzJ1RSaTYTp8OF1sbfEX\nKB6+f//+PHv2jF9//VUl7Sv7dwQwduxY4uLiOHTokFLbfR2+vr5ER0fz119/qcQnQhV9dOLECWbN\nmsWVK1fUOjGPulGxglcx3377LfXq1VNZyNnrjjQ33thIj909mH9hPhl5Gcw/P58eu3uw8cabt0nz\n8/Px8/OjX79+ghh3gCZNmtClSxc8PDxUJkPZR79ubm5YWloKYtxBkUrY3d2dsN27eZJa/uMFIZi2\ndi3ZeXnMnDVLMJkhISHcuHGDgwcPqqR9Zf+OLl26RExMjKBx8NOmTUMulxfKFaBMlN1HeXl5SKVS\nQkJCKox7KalYwQvAP//8Q+fOndm5c2cRj3pVIZfL2XtnL9JYKUlZSZjpm+HT0ofudbq/0dFJKpWy\nadMmTp06JWi2tNTUVDp27IirqysjRowQTG5Z+Ouvv/jhhx/YsWMHDRs2FFR29+7daWBkxAE1KqVb\nHKmZmZg4OODp5cWwYcMEle3p6UlMTAzHjh1Ty0IzL/P1119jbGzM2rVrBZW7Y8cOpk2bxsGDB9Wy\n+NXLrF+/niNHjnDo0CGVOWm+r6j3r/89oWnTpgwZMoSwsDDBZEokEiRISHuWhpWBFWnP0kDCG1+Q\nJ0+esHbtWry8vARPhWpgYMDIkSNZsGABubnKicFWFT4+PnTu3Flw4w6KVMKHL17kdJzyneGUycCg\nIGrWrCm4cQfFFnR2drbalUB9lS1btnDnzh0WLlwouOw+ffpgYWEharGbkvD06VPCw8MJDQ2tMO5l\noMLAC8SsWbP4448/VBpy9irxafHMbTeXnV/vZG67udxOK+p89DLu7u6YmZkVG0YjBE5OTlSqVOm1\nHunqQGRkJI8fPxYtZ/7zVMIDSpJBTyTO3brFvthYpC+lQhUSPT09XFxciIyMJD09/e0PiMDzbeeB\nAwdibGz89gdUwIIFCzhz5gynTp0SRX5JCA8Px97enhYtWoityjtJxRa9gISHh7Nu3Tp++eUXtZuN\nnjt3jiFDhrBp0yaaN28umh5//PEHLi4u/PHHH9SuXVs0PYojOzsba2trRowY8VqvfyF48uQJXTp3\nZtHYsYxRw3KoH48bh3GdOqxatUpUPbp27UrDhg1ZunSpqHoUh6+vL7/99hunTp0S9Rhh9OjR3Lp1\niwMHDoimw+u4fv06I0aM4OrVq5iYmIitzjtJxQpeQMaOHct///3H/v37xValCJ6enrRr105U4w7Q\nrVs3GjdujLOzs6h6FMeMGTPQ09PDyclJVD2qV6/OUAcHXH/5ReWphEvLqgMHiE9OJigoSGxVkEql\nxMTEEKdmxxnJycls2rSJadOmie4jMH/+fJKTk5U6GXsemluetaNcLicgIIBp06ZVGPdyUGHgBURL\nS4vQ0FCCgoLIyVFO4QZlEBUVxb1794pNECIG8+bN4+rVqxw5ckRsVQq4ffs2u3btYu7cuaIPyqBI\nJaylo8P4iAixVSlAJpPhtHw5gwYNUgvHrXbt2tGqVStRd1uKw93dHQsLC/r06SO2KlSpUgVHR0cW\nLFhAdnb22x8oAfvu7GP9jfXsu7uvzG0cOnSI5ORkJk6cqBSdPlTEH6k+ML788ktatmwp+vblc3Jy\ncggODsbBwYFq1aqJrQ4A9erV46uvvlKrs3g3Nzc+/vhjunbtKrYqgCJT4rRp01hx4AD3HhetJSAG\nTsuWgaYmPj4+YqtSQEhICAkJCSqLiy8tp06d4syZMyxYsEBsVQqYNGkSurq6zJw5s1ztlDU091Vy\nc3MJDAwkNDRUpWl7PwQqDLwIzJs3j5UrV/LgQdGc1EIze/ZsNDU1BalkVRrmzJlDRkYGixYtElsV\nDh48yD///CNoFERJ6NmzJ1b16/OdSM5sL5P89CnL9u5l6tSpKisoUxZq1qyJvb09c+bMUXoWwLLg\n4+NDx44dady4cbnaUcY2+HM0NDSYPXs20dHR3Llzp8ztDLAagFNzJ7LzFTsB2fnZODd3ZoDVgFK1\ns2bNGpo2barSIlcfChUGXgTq16/P6NGjRQmPeZl79+6xfft2Zs6cqRbbzi+jr6/Pjz/+yLJly8jI\neH2qS1Ujk8mYMWMGX331FXXq1BFNj9cRFBzMqbg4Doucn9s+MBDLunXVYtv5VaZOnYpcLsff379c\n7ZTXqK5atYrk5GTmKSFtrzK2wV+mW7duWFlZlStsriyhua/y6NEjIiMj1ea48F1HvUb1D4ipU6dy\n4sQJLly4IJoO7u7u1KtXj2+++UY0Hd7EyJEjMTIyEnV3ITw8nLS0NJVl/SovjRo1wrZrV4aqONf7\nmzh25Qp//vOPWjjWFYe2tjaenp5ERUXx5EnxxV5KQnmManZ2NgsWLMDR0ZEqVaqUWQdlbYMXR2ho\nKBcvXuTw4cNlbqO0obmvsnDhQkaMGCFKjon3kYowORFZtWoVCxYsYP369YKvoI8dO8bo0aOJjo7G\nyspKUNml4bmev/32Gw0aNBBUdnp6OjY2Nri4uDBy5EhBZZeG53rOGTIEDxHKaFqOGoVV06ZEqJHD\nX3F89dVX1KhRg9WrV5fquY03NrI6bjX5snzi0+OpV6UemhqaDG84nIENSpYzwtvbm8OHD3P8+PFy\nvetlzVBZUiZNmsSZM2c4evSo4GPSpUuXmDBhAnFxcRgaGgoq+32lYgUvIsOGDUNTU5OdO3cKKlcm\nkzFlyhS6du2q1sYdoEOHDnz22WeieEJ7eXlhZGSk1sYdFJ7Qo0ePZkZUFNkCZwEMi44mKSUFqUDF\nZMpDUFAQZ86cITY2tlTPlfds+c6dO0RHRzN79uxyG01lbIO/CalUSlpamuCTNblcjlQqxc/Pr8K4\nK5EKAy8iGhoahIWFMX/+fEHPmSMjI0lJSSEwMLDgmqOjBRcvKgo55OZC69YNiIx8kTd/2LA6XLum\nq1Q9/P1N2bDhxcu8cqURAwdaMnCgJeHhL7J7BQcHEx8fz+7du5Uq/01cu3aNQ4cOFdp2Voc+2rTJ\nkP79FX10+HDlgusTJ06kStWqfC+gd3Zubi7ea9bwww8/FERgqEMfrVtXDXt7SwYMsGTPnhfb4Z98\n8gkdOnTAzc2tVO2X16i6ubnRoEEDunXrVuReWfqrrNvgN27oMGRIHYYMqYOPjxn5+UU/o6enx48/\n/sjSpUsFHZN2797Ns2fPcHR0FEzmh0CFgRcZa2trvvjiCyIjIwWRl5WVRXh4OGPGjKFy5RcGwsYm\nkzNn9AE4e7YSnTplcPSo4n5OjoT797Vo1Eg5sftPnmgyenRtDh2qUlB56s4dbXburMqGDQls3JjA\nsWOVuHZNURD6eT30mTNnCuYJ7e7uTosWLWjbtm3BNbH76OFDTdauNSIqKoHIyLvMm2fK8wX783ro\nW48f5+b9+0rR4W2MDg9HT1+/0O6K2H2UkqLJhg3V2LAhgRUr7hAQUDgePzAwkIcPH7J+/fpSySqr\nUT18+DCXLl16rUNtWfprbLOx2NWxQyKRYFfHjjFNx5RIlwULTHBze8T69QpP+UOHivcFGD16NAYG\nBoKFO2ZlZTFv3jzCwsLUKgLjfaDCwKsBgYGBbNiwgbt376pc1rRp06hcuTI//vhjoes2NhmcPasY\naI4erUz//v+RlqZJeroGsbF6tG2bBYCtbX1GjbLA39+U69d1GDnSAkdHC/r0qUtsrGIl0rt3XZyd\nzbG3t2T69Jq86uWRmSnByekxvXqlFtyrVesZP/98t2CgzsuToKf34sEZM2aQn59faNdBVURHR3Pr\n1q0ig7LYfXTxoh6tWmWhrQ1VqsioWze30OrO1taWpk2bChI2l/DwIeuOHuWnVyIwxO4jI6N8fv01\nHk1NePhQCx2dwg8ZGRkxfPhwgoKCSpUFsCxGVSaTMW3aNLp37069evWK/YyQ/RUaeo/PP88iNxce\nPdKkatVilvD/T0BAAPv37+f69esl6J3y8csvv2BtbU2nTp1ULutDQ9iSYRUUi4WFBZMmTSIoKIjp\n06erTM79+/fZs2cPixcvLnKvSZMcbt1SrJjPnNFn8uSHWFtncvx4Ja5d06VTJ8V2XVKSFtu3x2No\nKOP336vi5ZVMw4a57NxZlW3bDGnZMpvERG1+/vkupqb5uLiYs39/Fbp1e1H0w8IiDwuLvIKVCoCW\nFlSrJkMuh8BAU5o2zaFu3WcF95/XQ/fz88POzq7Q7oMykclkzJ49m379+lGzZk216qOMDI1Cg3Kl\nSjLS0wuveIJDQvj6q6+I2L2bjk2bKr1/njN0/nwaNWxYJFZZ7D4C0NBQbNOHhRkzfPjTIrq7ubmx\ndetW3N3dVZp2eMuWLaSlpb3RP0HI/tLQgHv3tHB0rIOBQf4bd1JsbGxo3rw5rq6urFixQkk9UpTH\njx+zdu3aUvtFVFAyKgy8muDu7k50dDR9+/ZVmYycnJz/a+/O46Is1/+Bf2CYQVHBhU1F4acdwyVL\nTA1E7es5pn4VM1M0F9xQD+cklIiACi4sDrKDoOaLTDPTJJfQirJQySVAyTRLJRJxQQ31sK8z3z/m\np0di2GeeB8bP+69e88w818UdzsVzP/dz3bC0tMSYMWNqHdPXB2xty5GaagRT02rIZMCoUUVISemI\na9cMMX/+IwCqKyQTE9U0ubl5FbZu7QZDQ+X/Lz6q1/v2rYCZmaoQ2dmV4sYNWSPz08Pq1Zbo1EmB\ndetqNwGaOXMmgoODMWfOHK1u1qNQKPD+++/Xel3sMerQQYHi4v9eLZeU6MPYuOZVmLW1NSwsLfGv\n7du1ugpaoVCo7Xwm9hg9MWfOYzg7P8bSpVb48cf2GDGi9Jkc9TF27FgcPnwY33yjmefI1VEqlXBw\ncEC7du3qfI/Q49WjRxWSk/9AYqIJ5HJzyOV5debm5+eHWbNmafU7CQBWr14Na2trrcZ4XrHAtxLt\n27fX+raNJSUlsLW1RXp6OoYNG1bruINDCbZt6wYnpwIAwNChpYiLM4VEooSxsepL5NmaERRkjrCw\nu+jTpwKxsd1w+7aqrWROjhSFhaovnszMdnjrrYIGc1MqgX/9qyfs7Uvg6qr+WeWwsDBIpVJkZmZq\ndb/6cePGwcvLS+26CDHH6KWXyhAVZYaKCj2Ul+vh999l+Nvfal6FnT59Gvfu3UNSUpJWHytcuXIl\nYmJi1G4tLOYYZWdLERlphtjYOzAwAGQyJf56W7e4uBjHjh3D8uXL4ebm1swRaNjnn3+OdevW4c8/\n/6x3wxShxsvNrSd8fO7D2roSRkYKSCT1PyG9Z88eeHt7IygoqDk/PrUCvAf/HDEyMsLmzZshl8tR\nrWYJrb19MTIz22P0aNW0oFQKmJhUY9iw0lrvBYApUwrg4dEDS5f2hFKpuucJAIaGSnh7W2LmzN7o\n3r0KY8bUvRr3yYX48eMdkZHRHqmpRnBx6QUXl1746af/Xvk8evQIH3/8sWqTFS0Wd0B1//Hs2bP4\n5Zdfah0Tc4zMzKoxb94jzJnTCwsW9ML77/8J2V8u0nx8fPCPf/xD6z0DAgIC6mwlLOYY9elTCVvb\ncsyc2RvvvNMbr7xSildfVcV90oluzdo16Nixo1aLOwC8/fbb6N69O7y8vOp9n1DjtXRpPnx9LTF/\nvhW++MIY77//Z505ZWZmIj09vVXtK0BNx0Y3zxmlUomRI0di0qRJmD59ulZiODnZICnphkbPKfS+\n1QsXLsTt27e1NoWrjTHavn074uPjcebMGa2tUXhWQkICoqOjcfbsWa3E0/QYJd9Mhs85H5TuK8WO\n93cIsqjrl19+wYwZM7Bnzx7Y2dlpNZamxkuhUOCdd97BypUrMXfu3JYnRqLhFfxzRk9PD7GxsYiO\njkZhYaGWYmj2fBcvXsTp06c10sO7sUJDQ3Hnzh0kJiZq5fyaHqOSkhLEx8fjn//8pyDFHVC1Eu7a\ntavWWglraoyebe9aqiiFbKIM8kK5Rtq7NmTgwIGwt7eHt7e31mNpary++OILGBoaYvbs2Zo5IYmG\nV/DPqcWLF0NPT6/B6cPWYPz48bC0tBR8i93g4GB8/vnn+PHHH7V+W6ClPDw8cOHCBZw8eVLQFqNn\nzpyBq6urKK2EG+tJe9f1Z9fjsfIxzA3NsWboGo21d21IQUEBRo4cCW9v71Z/RVxUVITJkyfjyJEj\nNXpAUNvEK/jnVHBwMA4dOoTs7GyxU6nXZ599htu3b4uyu5S3tzckEgkCAgIEj90Uf/zxB7799lsE\nBgYK3j/cwcEBQ4YMEaWVcGPp6elBqVTicdljdCzriOLqYo22d1Xn2Z3njI2N4eLigoiICJSXa6bJ\nj7Z88MEH+Mc//sHiriNY4J9TFhYW8Pb2brU7gAFAZWUlQkJC8M4776Br166Cx5dIJFi9ejUSExPx\n4MEDweM3lru7O1566SW1jz8KITw8XPBWwk219+u9kH4pxel3Tjdrl7Om+uvOc56enpBKpdi4caNW\n47bEzZs3kZiYiBABGiWRMDhF/xwrLy/HwIED4ePj0yq7SK1fvx7Hjh3Djz/+KOp+9ZMmTYKJiUmT\n25sKITk5GStWrMDXX38t6n71fn5+SE5Oxrlz50T9f6XO48ePMWrUKKxatQrz5s3Taqz6dp4zyTKB\np6cnvvnmG/Ts2VOreTSHu7s7xowZg9WrV4udCmlI6/qXSIIyNDREZGQkQkJCUFlZ2fAHBJSXl4cD\nBw7A399f9IIRFhaGn376Cenp6Ro757NTuM2lUCjg7+8PJycnUYs7IGwr4aby9vaGqamp1os7UP/O\ncxMmTECfPn2wcuVKrefRVGfPnsW1a9ewYsUKsVMhDWKBf85NnjwZNjY2+PTTT8VOpYaVK1eid+/e\ncHJyEjsV9O/fH6+//rpGv5j/OoXbHJGRkaisrMSGDRs0lldzSaVSeHl5Yc+ePXj8uHZ7WLFcvnwZ\nqampCA8PFyReQzvPRUdH4+LFi/jhhx8EyacxqqqqIJfLER4eXm/XPWp7OEVPuHLlCkaPHo2kpCRR\n7nX/1blz57Bo0SIcPnwY/fr1EzsdAKqV0I6Ojnj//fdbtKVlfVO4M1+o3RWuvnxGjhwpyLRzU4wf\nPx49evTQav/yppg4cSK6deuGPXv2CBZz+y/bYWNsgzes3sA3t75BTmFOjc1p3n33Xfz88884ceKE\n6LNTALB3716cPHkSKSkpgjxVQMIR/7eLRDdgwADMnj1bbVcyMfj6+mLMmDEtLu6amAZ/wtjYGK6u\nroiOjkbFkz1am6G+KdymWLFiBSwsLDRe3Fs6Zps3b8aPP/6Iy5cvazSv5vj888+Rm5tb51at2tLQ\nznObN2/G48ePsWPHDkHzUufx48eIj49HTEwMi7sOYoEnAKoFbd9++y2uXr3aovO0tEAkJCQgPz8f\nYWFhLcoD0Mw0+LPeffddGBkZtWgRUkNTuI1x5coVnDlzpt5dypqrpWP28ssvw97eXu1mPUKqrq6G\nXC6Hs7MzunXrJmouf2VkZAQ3NzfEx8ejtFR9O1qhxMfHY/r06Rg8eLCoeZB2cIqenoqLi8Mnn3yC\nDz/8sNl/zSffTMbqtNUIHhGM8b3GN/yBZ5SVleG1117DokWL4O7u3qz4gOamwdU5fvw4PDw8kJyc\nDCsrq2ado6Ep3IZoo/GPJsfs4cOHGD16NPz8/NRuRiOEgIAAHDlyBGlpaa1iGlwdR0dHDBs2TJQe\nDwCQlZWFBQsW4Ndff613Mxxqu1jg6amqqiq8/PLL+Oc//4k33nijSZ/VRIHw8vLC6dOn8cMPP7To\nS/lJ5zJ5phx5pXmwbG8J3yG+Gutc9vbbb0OpVOLgwYMtPldTHTp0CH5+fvj+++9hbm6usfNqesxC\nQkKwf/9+pKWlCd4F8P79+xg7diwCAwMxdepUQWM3RWpqKpYtW4akpCT07dtX0NhKpRJLlizB22+/\njffee0/Q2CSc1vmnLYnCwMAAMTExCA0NbXLHrZbeW87JycGXX36JoKCgFl9xaWIavD4RERG4evUq\nTpw4oZHzNZZCoUBgYCBmzZql0eIOaH7MVq5cCalUKsoK/5UrV8LKyqpVF3cAGDVqFAYMGCDKY3Mp\nKSm4f/8+/v3vfwsem4TDAk81/P3vf4ednR0++uijJn2upQXC09MT/fr1w//8z//UOrZwoRUuXVI9\nvlNRAbz66gtISOjy9Pi8eb1w9aphjc/cKLyB4BHBODrxaJM6l2VlyTB7di/Mnt0Lvr6WULOrLqyt\nrTFx4kSsXr1aIwv4GisgIAD6+vpqt/Bszhj9VWPH7LffDDFzZm/MmdMLq1dbQt2aQ4lEAj8/Pxw8\neBD37t1rwk/ZMmlpacjIyEBUVJTa45oYp6ZKSuqEWbN6qz0WGxuLa9eu4euvv9ZozPpUVFRg8+bN\niImJgVQqFSwuCY8FnmqJiIjARx991OQv5uYW1e+++w5XrlxBTEyM2uMODiXIyGgPADh/3gijRhXj\n1CnVjmnl5Xq4e9cAL75Yc8ahoZXMdYmKMoWn55/YuzcXAJCS0lHt+wICAlBSUiLYkwf5+fn47LPP\n4OvrC4lEUut4c8borxo7ZmvXWsDX9z4++SQXFhaV+PTTLmrfN3nyZPTp00fQPvU+Pj4YOXIkbG1t\n1R7XxDg1xZUrhjh40KTO4927d8fkyZOxYcMGKBQKjcWtz8cff4zlG84hAAAbDUlEQVQBAwZg/Pim\nrZGhtocFnmrp06cPli5dWudVUF2aU1SfdGObMGFCnd3YHByKcf686kv51KkOmD79PygslKCoSB+Z\nme0wfLhqJfLYsX3g6mqFTZvMcP26DIsXW2HhQitMnWqNzEzVVdubb1rD3b0HZszoDT8/C/z1Ajwm\n5g6GDi1FRQXw558SdOqk5hIeQPv27bF8+XLs2LEDxcXFjR2iZvPw8IC1tTWmTZum9riQY3TvnhSv\nvKK6FTNkSBnS09vXmXdYWBh+/vlnpKWltXQIGrRr1y48ePCg3kVrQo7To0f6iIoyha/v/VrHnhUQ\nEICysrIm/3trjj///BMJCQmiLewjYbHAk1pr1qzBuXPncPHiRa3GiYuLQ3FxMYKDg+t8T//+5cjO\nlgEAMjLaY9iwEtjbl+DMGSOkp6uuwgAgL88A4eF34Ov7AFlZhvD2vo+dO2/B1fXh06uo27el8PO7\nhwMHbqKgQILjx2teoevrA3fuGMDJ6f/h8WNJvVdzT/ZD1/aWuxkZGbhw4UK9GwMJOUZWVpVPi3pK\nSgeUlNT9NfLiiy9i7NixWh+jJwVy4cKF6NhR/awLINw4VVcDa9dawsfnAYyM6r8yl8lkWLFiBXbu\n3Kn1LoDR0dFYsGBBq2kgRdrVuje5JtF06tQJoaGhcHFx0eoq6NLSUixcuLDeFpn6+oCtbTlSU41g\naloNmQwYNaoIKSkdce2aIebPfwQA6NKlGiYmqi9Tc/MqbN3aDYaGShQX66NTJ9XrfftWwMxMdVVu\nZ1eKGzdkteL16FGF5OQ/kJhoArncHHJ5Xp25LVu2DBs3bkT//v2bPQYNUSqV6Nu3LwYOHFjne4Qc\no+DguwgONkd8vB6GDi1FQYH6WY4nNmzYAEdHR62OEaBaJNrQinChxumXX9rh5k0Z1q+3QHm5Hn7/\nXYZNm8zg66t+V8J58+Zh69atGD16tFbvi3fu3Bm//PKL1s5PrQsLPNVpzpw5mDp1qlbvDe7duxcf\nfPABlEplvQvyHBxKsG1bNzg5FQAAhg4tRVycKSQSJYyNVfk9u/g+KMgcYWF30adPBWJju+H2bdWX\nZk6OFIWFqi/pzMx2eOutghpx3Nx6wsfnPqytK2FkpIBEUv8iut27d+OVV17R6vTquXPnsHr1auTk\n5MDa2rrO9wk1RidOdERo6F107qxAYKA5HB3rv0URGBiIbt264dChQ1rrllZSUoLJkydj165dDbYS\nFmKcBg8uQ1LSDQDA7dsGWLGiR53FHVA1L5JIJMjKykLnzp2bM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       ],
       "text": [
        "Cluster(cell_radius=1.0,num_cells=19,pos=0j,cluster_id=None,cell_type='simple',rotation=None)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculate the distances between each user and each base station"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multiUserChannel.randomize(Nr, Nt, K)\n",
      "\n",
      "if include_path_loss_bool is True:\n",
      "    # xxxxxxxxxx Calculate the pathloss for each link xxxxxxxxxxxxxxxxxxxxx\n",
      "    dists = cluster.calc_dist_all_users_to_each_cell()\n",
      "    #pl_obj = pathloss.PathLossFreeSpace()\n",
      "    pl_obj = pathloss.PathLoss3GPP1()\n",
      "    #pl_obj = pathloss.PathLossOkomuraHata()\n",
      "    # Calculate the path loss in linear scale\n",
      "    pl = pl_obj.calc_path_loss(dists)\n",
      "\n",
      "    #pl = np.eye(19)\n",
      "    multiUserChannel.set_pathloss(pl)\n",
      "else:\n",
      "    # Path loss will be equal to 1.0 for all links\n",
      "    pl = np.ones([K,K])\n",
      "    multiUserChannel.set_pathloss(None)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot the distances"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fig, axs = plt.subplots(10,2, figsize=(12,40))\n",
      "# for row in range(10):\n",
      "#     for col in range(2):\n",
      "#         index = 2 * row + col\n",
      "#         if index > 18:\n",
      "#             break\n",
      "# #         print \"{0}: {1}x{2}\".format(index, row, col)\n",
      "#         axs[row, col].set_xlim([0, 20])\n",
      "#         axs[row, col].grid()\n",
      "#         axs[row, col].plot(range(1,20), dists[index])\n",
      "#         axs[row, col].plot(index+1, dists[index][index], 'or')\n",
      "#         axs[row, col].xaxis.set_ticks(np.arange(1,20))\n",
      "# plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculate the Transmit power of each user"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculate the transmit power of each user\n",
      "snr = dB2Linear(SNR)\n",
      "Pt = snr * noise_var/pl.diagonal()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Path Loss of the Interference Links relative to the direct link"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl_dB = -linear2dB(pl)\n",
      "np.set_printoptions(precision=1, linewidth=140)\n",
      "\n",
      "for index in range(19):\n",
      "    pl_dB[index] = pl_dB[index] - pl_dB[index][index]\n",
      "\n",
      "print 'Relative Direct Channel Path Loss:\\n{0}'.format(pl_dB.diagonal())\n",
      "print 'Relative Max Path Loss:\\n{0}'.format(pl_dB.max(axis=1))\n",
      "print 'Relative Min Path Loss:\\n{0}'.format(np.sort(pl_dB, axis=1)[:,1])\n",
      "print\n",
      "np.set_printoptions(precision=4, linewidth=120)\n",
      "# print np.mean(np.sort(pl_dB, axis=1)[:,1])\n",
      "# print np.mean(pl_dB.max(axis=1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Relative Direct Channel Path Loss:\n",
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        "Relative Max Path Loss:\n",
        "[ 54.2  28.2  43.1  29.5  45.9  27.6  46.7  28.3  41.8  34.2  27.2  30.2  25.7  30.4  37.4  27.   27.6  36.2  40.3]\n",
        "Relative Min Path Loss:\n",
        "[ 41.    4.8  27.9   8.1  31.3   3.2  32.5   5.6  26.5  15.4   3.3   8.5   0.3   9.9  20.1   2.4   3.3  18.5  24.2]\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Question: How many interference links are strong enough?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First we need to ask ourselves what is a good measurement that we can use to say that a interference link is strong enough or not. The easiest one is probably the path loss.\n",
      "\n",
      "If we count the number of interference links that have a relative path loss when compared with the direct channel of less than 30dB we get"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[(sum(pl_dB[i] < 30) - 1) for i in range(19)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "[0, 18, 3, 18, 0, 18, 0, 18, 4, 10, 18, 17, 18, 17, 6, 18, 18, 7, 5]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, notice that the relative path loss is only one side of the coint. We still need to account the transmit power of each interfering base station when compared with the transmit power of our own base station. That is, if the relative path loss to some interference link is 25dB, but that particular base station transmit with 10dB more power than our own base station then the relative interference power of that link when compared with the desired signal would be 15dB."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Find the IA Solution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We wouldn't need to explicitly set ia_solver.noise_var\n",
      "# variable if the multiUserChannel object had the correct value at\n",
      "# this point.\n",
      "ia_solver.noise_var = noise_var\n",
      "ia_solver.clear()\n",
      "runned_reps = ia_solver.solve(Ns=Ns, P=Pt)\n",
      "print runned_reps"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "500\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some of the precoders may have 0 energy."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = []\n",
      "for f in ia_solver.full_F:\n",
      "    data.append(np.linalg.norm(f, 'fro')**2)\n",
      "    print data[-1]\n",
      "    \n",
      "print\n",
      "data = np.array(data)\n",
      "#print \"Number of non zero links: {0}\".format(len(data[data > 1e-3]))\n",
      "print \"Sum Capacity: {0}\\nxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\".format(ia_solver.calc_sum_capacity())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00689926118772\n",
        "4.89008163273\n",
        "0.101411735891\n",
        "3.3584047519\n",
        "0.0516427810224\n",
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        "0.0415080027238\n",
        "4.780675299\n",
        "0.138287209767\n",
        "0.990997754796\n",
        "6.38687999569\n",
        "2.83939726322\n",
        "9.87741846757\n",
        "2.65742424189\n",
        "0.431858619226\n",
        "6.86329248688\n",
        "5.8463990638\n",
        "0.576853235642\n",
        "0.204417858905\n",
        "\n",
        "Sum Capacity: 245.334214211\n",
        "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pos Processing SINR for each Stream"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Input SNR: {0}\".format(SNR)\n",
      "\n",
      "print \"Post-processing SINRs\"\n",
      "for v in ia_solver.calc_SINR_in_dB():\n",
      "    print v"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Input SNR: 30.0\n",
        "Post-processing SINRs\n",
        "[ 18.7826  12.3742]\n",
        "[ 20.608   23.8472]\n",
        "[ 14.3746   8.838 ]\n",
        "[ 25.489   23.8495]\n",
        "[ 12.0019  14.442 ]\n",
        "[ 23.0137  26.7986]\n",
        "[ 18.138   15.6188]\n",
        "[ 23.1094  22.733 ]\n",
        "[ 13.4218  11.6146]\n",
        "[ 20.6801  13.4515]\n",
        "[ 23.6831  24.6392]\n",
        "[ 25.2008  21.2369]\n",
        "[ 24.5851  24.7833]\n",
        "[ 22.0793  18.3695]\n",
        "[ 15.0537  14.3565]\n",
        "[ 21.4864  23.5646]\n",
        "[ 24.5577  23.0609]\n",
        "[ 18.713   13.2266]\n",
        "[ 15.8891  17.368 ]\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# # If any of the Nr, Nt or Ns variables were integers (meaning all\n",
      "# # users have the same value) we will convert them by numpy arrays\n",
      "# # with correct size (K).\n",
      "# Nr = ia_solver.Nr\n",
      "# Nt = ia_solver.Nt\n",
      "# Ns = ia_solver.Ns\n",
      "\n",
      "# cumNs = np.cumsum(ia_solver.Ns)\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # xxxxx Input Data xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# # inputData has the data of all users (vertically stacked)\n",
      "# inputData = np.random.randint(0, M, [np.sum(Ns), NSymbs])\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # xxxxx Modulate input data xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# # modulatedData has the data of all users (vertically stacked)\n",
      "# modulatedData = modulator.modulate(inputData)\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # xxxxxxxxxx Perform the Interference Alignment xxxxxxxxxxxxxxxxxxx\n",
      "# # Split the data. transmit_signal will be a list and each element\n",
      "# # is a numpy array with the data of a user\n",
      "# transmit_signal = np.split(modulatedData, cumNs[:-1])\n",
      "# transmit_signal_precoded = map(np.dot, ia_solver.full_F, transmit_signal)\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # xxxxx Pass through the channel xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# multi_user_channel = ia_solver._multiUserChannel\n",
      "# # received_data is an array of matrices, one matrix for each receiver.\n",
      "# #import pudb; pudb.set_trace()  ## DEBUG ##\n",
      "\n",
      "# received_data = multi_user_channel.corrupt_data(\n",
      "#     transmit_signal_precoded, noise_var)\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # xxxxx Perform the Interference Cancelation xxxxxxxxxxxxxxxxxxxxxx\n",
      "# received_data_no_interference = map(np.dot,\n",
      "#                                     ia_solver.full_W_H, received_data)\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # xxxxx Demodulate Data xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# received_data_no_interference = np.vstack(received_data_no_interference)\n",
      "# demodulated_data = modulator.demodulate(received_data_no_interference)\n",
      "# # demodulated_data = map(modulator.demodulate, received_data_no_interference)\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # xxxxx Calculates the symbol and bit error rates xxxxxxxxxxxxxxxxx\n",
      "# symbolErrors = np.sum(inputData != demodulated_data)\n",
      "# bitErrors = misc.count_bit_errors(inputData, demodulated_data)\n",
      "# numSymbols = inputData.size\n",
      "# numBits = inputData.size * modulators.level2bits(M)\n",
      "# ia_cost = ia_solver.get_cost()\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # xxxxxxxxxx Calculates the Sum Capacity xxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# sirn_all_k = ia_solver.calc_SINR()\n",
      "# calc_capacity = lambda sirn: np.sum(np.log2(1 + sirn))\n",
      "# # Array with the sum capacity of each user\n",
      "# sum_capacity = map(calc_capacity, sirn_all_k)\n",
      "# # Total sum capacity\n",
      "# total_sum_capacity = np.sum(sum_capacity)\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# #xxxxxxxxxx Number of iterations of the IA algorithm xxxxxxxxxxxxxx\n",
      "# ia_runned_iterations = ia_solver.runned_iterations\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "\n",
      "# # Print the results\n",
      "# print(\"SumCapacity: {0}\".format(total_sum_capacity))\n",
      "# print(\"BER: {0}\".format(float(bitErrors)/numBits))\n",
      "\n",
      "\n",
      "# # xxxxx Return the simulation results xxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# symbolErrorsResult = Result.create(\n",
      "#     \"symbol_errors\", Result.SUMTYPE, symbolErrors)\n",
      "\n",
      "# numSymbolsResult = Result.create(\n",
      "#     \"num_symbols\", Result.SUMTYPE, numSymbols)\n",
      "\n",
      "# bitErrorsResult = Result.create(\"bit_errors\", Result.SUMTYPE, bitErrors)\n",
      "\n",
      "# numBitsResult = Result.create(\"num_bits\", Result.SUMTYPE, numBits)\n",
      "\n",
      "# berResult = Result.create(\"ber\", Result.RATIOTYPE, bitErrors, numBits,\n",
      "#                           accumulate_values=False)\n",
      "\n",
      "# serResult = Result.create(\n",
      "#     \"ser\", Result.RATIOTYPE, symbolErrors, numSymbols, accumulate_values=False)\n",
      "\n",
      "# ia_costResult = Result.create(\n",
      "#     \"ia_cost\", Result.RATIOTYPE, ia_cost, 1, accumulate_values=False)\n",
      "\n",
      "# sum_capacityResult = Result.create(\n",
      "#     \"sum_capacity\", Result.RATIOTYPE, total_sum_capacity, 1,\n",
      "#     accumulate_values=False)\n",
      "\n",
      "# ia_runned_iterationsResult = Result.create(\n",
      "#     \"ia_runned_iterations\", Result.RATIOTYPE, ia_runned_iterations, 1, accumulate_values=False)\n",
      "\n",
      "# #import pudb; pudb.set_trace()  ## DEBUG ##\n",
      "\n",
      "# simResults = SimulationResults()\n",
      "# simResults.add_result(symbolErrorsResult)\n",
      "# simResults.add_result(numSymbolsResult)\n",
      "# simResults.add_result(bitErrorsResult)\n",
      "# simResults.add_result(numBitsResult)\n",
      "# simResults.add_result(berResult)\n",
      "# simResults.add_result(serResult)\n",
      "# simResults.add_result(ia_costResult)\n",
      "# simResults.add_result(sum_capacityResult)\n",
      "# simResults.add_result(ia_runned_iterationsResult)\n",
      "# # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# # return simResults"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    }
   ],
   "metadata": {}
  }
 ]
}